CN112866581A - Camera automatic exposure compensation method and device and electronic equipment - Google Patents
Camera automatic exposure compensation method and device and electronic equipment Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/70—Circuitry for compensating brightness variation in the scene
- H04N23/71—Circuitry for evaluating the brightness variation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/161—Detection; Localisation; Normalisation
- G06V40/166—Detection; Localisation; Normalisation using acquisition arrangements
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- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/168—Feature extraction; Face representation
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- H—ELECTRICITY
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- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/60—Control of cameras or camera modules
- H04N23/61—Control of cameras or camera modules based on recognised objects
- H04N23/611—Control of cameras or camera modules based on recognised objects where the recognised objects include parts of the human body
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/70—Circuitry for compensating brightness variation in the scene
- H04N23/73—Circuitry for compensating brightness variation in the scene by influencing the exposure time
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Abstract
The application provides a camera automatic exposure compensation method, a camera automatic exposure compensation device and electronic equipment, wherein the camera automatic exposure compensation method is based on a target detection and identification system and comprises the following steps: acquiring a face image through a camera; training a target detection and recognition system and generating a strong backlight face detection model; the target detection and recognition system detects the brightness of the imaging position of the human face; the target detection and identification system judges whether the difference value between the brightness value of the face imaging position and the ideal brightness value meets a preset value; if so, the target detection and recognition system carries out face recognition on the image of the face imaging position; if not, firstly adjusting the gain and exposure parameters of the camera, and then repeating the steps S1-S4 until the face recognition is finished; the camera automatic exposure compensation method solves the problem that the face imaging is too dark in a strong backlight environment, improves the accuracy of the algorithm in a complex environment, and improves the application scene of the video image processing algorithm.
Description
Technical Field
The present application belongs to the field of face recognition technology, and more particularly, to an automatic exposure compensation method and apparatus for a camera, and an electronic device.
Background
The face recognition technology is a computer technology for recognizing faces by using analysis and comparison. Face recognition is a popular research field of computer technology, including face tracking detection, image processing algorithm, automatic image adjustment and amplification, night infrared detection and other technologies.
In the prior art, an image processing algorithm data source in the face recognition technology is basically from a camera, and along with the increasingly complex application scene, the model and the type of the camera are not fixed, and the finally generated imaging quality is unstable under the influence of camera parameters and ambient light, so that the imaging quality of the existing common camera in the strong backlight environment is poor, and the face recognition accuracy is low.
Disclosure of Invention
An object of the embodiments of the present application is to provide a method and an apparatus for compensating for automatic exposure of a camera, and an electronic device, so as to solve the technical problems of poor imaging quality and low face recognition accuracy of the camera in a strong backlight environment in the prior art.
In order to achieve the purpose, the technical scheme adopted by the application is as follows: the camera automatic exposure compensation method based on the target detection and identification system comprises the following steps:
step S1: acquiring a face image through a camera;
step S2: the target detection and recognition system uses a residual error network model in a deep learning target detection algorithm to carry out data acquisition training on the human face imaging characteristics in the strong backlight environment and generate a strong backlight human face detection model; if the face imaging characteristics can be detected, acquiring the face imaging position of the current face; if the human face imaging characteristics cannot be detected, detecting the human face imaging position under strong backlight by using a strong backlight human face detection model;
step S3: the target detection and recognition system detects the brightness of the imaging position of the human face;
step S4: the target detection and identification system judges whether the difference value between the brightness value of the face imaging position and the ideal brightness value meets a preset value; if so, the target detection and recognition system carries out face recognition on the image of the face imaging position; if not, firstly adjusting the camera gain and exposure parameters, and then repeating the steps S1-S4 until the face recognition is finished.
Preferably, the brightness value of the face imaging position is an average brightness value among brightness values of the pixels at the face imaging position.
Preferably, before the step S1, the method further comprises the step S01: and setting an ideal brightness value in the target detection and identification system, wherein the ideal brightness value is used for comparing with the brightness value of the human face imaging position.
Preferably, the setting program of the ideal brightness value is automatically changed according to the time point according to the rule.
Preferably, before the step S1, the method further comprises the step S02: and setting a preset value in the target detection and identification system, wherein the preset value is used for comparing the difference between the brightness value of the face imaging position and the ideal brightness value.
Preferably, the preset values are range values.
Preferably, in step S4, the adjusting the camera gain and exposure parameters includes: if the brightness value of the face imaging position is larger than the ideal brightness value, the camera gain and exposure parameter are reduced; and if the brightness value of the face imaging position is smaller than the ideal brightness value, the camera gain and exposure parameter are increased.
Preferably, the exposure parameter of the camera includes at least one of aperture, shutter speed, and sensitivity.
The application still provides a camera automatic exposure compensation arrangement, camera automatic exposure compensation arrangement includes camera and target detection identification system, target detection identification system includes face formation of image position and obtains module, detection module and judging module.
The system comprises a camera, a face imaging position acquisition module, a detection module and a judgment module, wherein the camera is used for image acquisition, the face imaging position acquisition module is used for carrying out data acquisition training on face imaging characteristics in a strong backlight environment through a residual error network model in a deep learning target detection algorithm and generating a strong backlight face detection model, the detection module is used for detecting the brightness of a face imaging position, and the judgment module is used for judging whether a difference value between the brightness value of the face imaging position and an ideal brightness value meets a preset value or not; if so, the target detection and recognition system carries out face recognition on the image of the face imaging position; if not, firstly adjusting the camera gain and exposure parameters, and then repeating the steps S1-S4 until the face recognition is finished.
The present application also provides an electronic device comprising a processor, a storage medium and a bus, wherein the storage medium stores machine-readable instructions executable by the processor, and when the electronic device is operated, the processor and the storage medium communicate with each other through the bus, and the processor executes the machine-readable instructions to perform the method as described above.
The camera automatic exposure compensation method provided by the application has the beneficial effects that: compared with the prior art, the method comprises the steps that the face imaging position of the current face is obtained through a residual error network model in a deep learning target detection algorithm; judging whether the difference value between the brightness value of the face imaging position and the ideal brightness value meets a preset value, and adjusting the gain and exposure parameters of the camera to complete face identification; the problem that the face imaging is too dark in a backlight environment is solved, the imaging quality difference caused by the software and hardware difference of various cameras is avoided, meanwhile, the accuracy of the algorithm in a complex environment is improved, and the application scene of the video image processing algorithm is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
Fig. 1 is a schematic step diagram of an automatic exposure compensation method for a camera according to an embodiment of the present disclosure;
FIG. 2 is a schematic diagram of an algorithm of the automatic exposure compensation method of the camera in FIG. 1;
fig. 3 is a schematic step diagram of an automatic exposure compensation method for a camera according to another embodiment of the present application;
fig. 4 is a schematic view of an automatic exposure compensation apparatus for a camera according to an embodiment of the present disclosure;
fig. 5 is a schematic diagram of the electronic device in fig. 1.
Detailed Description
In order to make the technical problems, technical solutions and advantageous effects to be solved by the present application clearer, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
It will be understood that when an element is referred to as being "secured to" or "disposed on" another element, it can be directly on the other element or be indirectly on the other element. When an element is referred to as being "connected to" another element, it can be directly connected to the other element or be indirectly connected to the other element.
It will be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like, as used herein, refer to an orientation or positional relationship indicated in the drawings that is solely for the purpose of facilitating the description and simplifying the description, and do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus should not be considered as limiting the present application.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present application, "a plurality" means two or more unless specifically limited otherwise.
Referring to fig. 1 and fig. 2 together, a method for compensating for an automatic exposure of a camera according to an embodiment of the present application will be described. The camera automatic exposure compensation method is based on a target detection and identification system and comprises the following steps:
step S1: acquiring a face image through a camera;
step S2: the target detection and recognition system uses a residual error network model in a deep learning target detection algorithm to carry out data acquisition training on the human face imaging characteristics in the strong backlight environment and generate a strong backlight human face detection model; if the face imaging characteristics can be detected, acquiring the face imaging position of the current face; if the human face imaging characteristics cannot be detected, detecting the human face imaging position under strong backlight by using a strong backlight human face detection model;
step S3: the target detection and recognition system detects the brightness of the imaging position of the human face;
step S4: the target detection and identification system judges whether the difference value between the brightness value of the face imaging position and the ideal brightness value meets a preset value; if so, the target detection and recognition system carries out face recognition on the image of the face imaging position; if not, firstly adjusting the camera gain and exposure parameters, and then repeating the steps S1-S4 until the face recognition is finished.
The face imaging can occupy a plurality of pixel points, and therefore the brightness value of the face imaging position can be the maximum value of the brightness values of the pixel points of the face imaging position, and can also be the average brightness value of the pixel points, so that the brightness of the corresponding picture can be represented.
Preferably, the brightness value of the face imaging position is an average brightness value among brightness values of the pixels at the face imaging position.
Accordingly, the average luminance value of the face imaging position can be calculated by averaging the natural logarithm of the luminance value Lum (x, y) of each pixel of the face imaging position. Since the calculation formula and method of the average brightness value Lumave of the face imaging position are the prior art, detailed description is omitted here.
According to the automatic exposure compensation method for the camera, the face imaging position of the current face is obtained through a residual error network model in a deep learning target detection algorithm, and then the camera gain and exposure parameters are adjusted according to the brightness value of the face imaging position, so that the definition of the face image can be adjusted to the best effect. Compared with the traditional picture global exposure, the method can not be influenced by the brightness of the background environment, has higher accuracy and can adapt to different scenes.
The target detection and recognition system uses a residual error network model in a deep learning target detection algorithm to carry out data acquisition training on the human face imaging characteristics in the strong backlight environment and generate a strong backlight human face detection model. Among other things, the residual network is characterized by easy optimization and can improve accuracy by adding comparable depth. The residual block inside the human face detection model uses jump connection, the gradient disappearance problem caused by increasing depth in a deep neural network is relieved, and the quality effect of the human face detection model is optimal when the human face imaging characteristics under the strong backlight environment are subjected to data acquisition training and generated.
It should be noted that the camera should be a non-infrared camera, because the infrared camera cannot clearly image in a strong light environment due to technical limitations, and therefore, this method is not suitable for the infrared camera.
Compared with the prior art, the camera automatic exposure compensation method has the advantages that the face imaging position of the current face is obtained through a residual error network model in a deep learning target detection algorithm; judging whether the difference value between the brightness value of the face imaging position and the ideal brightness value meets a preset value, and adjusting the gain and exposure parameters of the camera to complete face identification; the problem that the face imaging is too dark in a backlight environment is solved, the imaging quality difference caused by the software and hardware difference of various cameras is avoided, meanwhile, the accuracy of the algorithm in a complex environment is improved, and the application scene of the video image processing algorithm is improved.
In another embodiment of the present application, referring to fig. 3, before step S1, the method further includes step S01: and setting an ideal brightness value in the target detection and identification system, wherein the ideal brightness value is used for comparing with the brightness value of the human face imaging position.
It can be understood that the ideal brightness value can be set according to the ambient brightness in the actual situation, or the program can be set to automatically change according to the time point and the rule. For example, at daytime, the ambient brightness is higher, and the ideal brightness value may be higher by a little; at night, the ambient brightness is lower, and the ideal brightness value can be lower by a little; the method aims to ensure that the image at the face imaging position is clearer.
In another embodiment of the present application, referring to fig. 3, before step S1, the method further includes step S02: and setting a preset value in the target detection and identification system, wherein the preset value is used for comparing the difference between the brightness value of the face imaging position and the ideal brightness value.
It will be appreciated that the preset value may be a fixed value, but is preferably a range value. For example, the preset value may be a range value of 0 to 5 (i.e., whether the above absolute value is in the range of 0 to 5) or the like, or the preset value may be a fixed value of 0 (i.e., whether the above absolute value is equal to 0) or the like. Of course, the above is only an example provided by the present embodiment, and specific values of the preset value are not limited herein, and a person skilled in the art may set the preset value according to actually defined error ranges, data calculation amounts, and other factors. For example, in consideration of the amount of data calculation, the preset value may be set as a range value within a defined error range, and when the absolute value satisfies the range value, the camera gain and exposure parameters may not be adjusted, so as to reduce the number of times of automatic exposure adjustment of the camera, and reduce the number of times of calculating the average brightness value of the image at the face imaging position, thereby reducing the total amount of data calculation and improving the response time.
In another embodiment of the present application, in step S4, the adjusting the camera gain and exposure parameters includes: if the brightness value of the face imaging position is larger than the ideal brightness value, the camera gain and exposure parameter are reduced; and if the brightness value of the face imaging position is smaller than the ideal brightness value, the camera gain and exposure parameter are increased.
It should be noted that, in the above steps, the brightness value of the face imaging position is greater than the magnitude between the ideal brightness values, and the determination may be directly performed according to the difference between the brightness value of the face imaging position and the ideal brightness value. For example, the difference between the luminance value of the face imaging position being greater than the ideal luminance value is negative, i.e., the luminance value of the face imaging position is less than the ideal luminance value; accordingly, the difference is positive, i.e., the luminance value of the face imaging position is greater than the ideal luminance value.
In another embodiment of the present application, the exposure parameter of the camera includes at least one of aperture, shutter speed, and sensitivity.
It is understood that the influence of the aperture, shutter speed, and sensitivity on the exposure amount can be divided into several stages, and the exposure amount between adjacent stages differs by one time. The reasonable exposure parameter can adapt to image processing under different illumination intensities.
Aperture: the larger the aperture (the smaller the value, as F4), the more the exposure amount of the picture increases; the smaller the aperture (the larger the value, as F16), the less the exposure amount of the picture decreases. Common gears are F2.8, F4, F5.6, F8, F11, F16, F22 and the like, and the aperture is opened when the exposure is increased and is closed when the exposure is decreased.
Shutter speed: the shutter speed is the exposure time, the exposure time is doubled, and the exposure quantity is doubled. Common gears include 1 second, 1/2 seconds, 1/4 seconds, 1/8 seconds, 1/15 seconds, 1/30 seconds, 1/60 seconds, 1/125 seconds, 1/250 seconds, 1/500 seconds, 1/1000 seconds and the like, and of course, the exposure time is longer or more, and the length of the exposure time is adjusted according to the exposure amount of a picture, so that normal exposure is achieved.
Sensitivity: sensitivity is often set to a lower value, but ISO needs to be adjusted if not already available. Common gears are 100, 200, 400, 800, 1600, 3200, 6400 and the like, and adjacent gears are different by one time in numerical value and are different by one time in exposure amount.
Referring to fig. 4, the present application further provides an automatic exposure compensation device for a camera, where the automatic exposure compensation device for a camera includes a camera and a target detection and recognition system, and the target detection and recognition system includes a face imaging position obtaining module, a detection module, and a judgment module.
The system comprises a camera, a face imaging position acquisition module, a detection module and a judgment module, wherein the camera is used for image acquisition, the face imaging position acquisition module is used for carrying out data acquisition training on face imaging characteristics in a strong backlight environment through a residual error network model in a deep learning target detection algorithm and generating a strong backlight face detection model, the detection module is used for detecting the brightness of a face imaging position, and the judgment module is used for judging whether a difference value between the brightness value of the face imaging position and an ideal brightness value meets a preset value or not; if so, the target detection and recognition system carries out face recognition on the image of the face imaging position; if not, firstly adjusting the camera gain and exposure parameters, and then repeating the steps S1-S4 until the face recognition is finished.
Referring to fig. 5, the present application further provides an electronic device, which includes a processor, a storage medium and a bus, where the storage medium stores machine-readable instructions executable by the processor, and when the electronic device runs, the processor and the storage medium communicate with each other through the bus, and the processor executes the machine-readable instructions to perform the method described above.
The above description is only exemplary of the present application and should not be taken as limiting the present application, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (10)
1. A camera automatic exposure compensation method based on a target detection and identification system comprises the following steps:
step S1: acquiring a face image through a camera;
step S2: the target detection and recognition system uses a residual error network model in a deep learning target detection algorithm to carry out data acquisition training on the human face imaging characteristics in the strong backlight environment and generate a strong backlight human face detection model; if the face imaging characteristics can be detected, acquiring the face imaging position of the current face; if the human face imaging characteristics cannot be detected, detecting the human face imaging position under strong backlight by using a strong backlight human face detection model;
step S3: the target detection and recognition system detects the brightness of the imaging position of the human face;
step S4: the target detection and identification system judges whether the difference value between the brightness value of the face imaging position and the ideal brightness value meets a preset value; if so, the target detection and recognition system carries out face recognition on the image of the face imaging position; if not, firstly adjusting the camera gain and exposure parameters, and then repeating the steps S1-S4 until the face recognition is finished.
2. The automatic exposure compensation method for a camera according to claim 1, wherein the luminance value of the face imaging position is an average luminance value among luminance values of pixels at the face imaging position.
3. The camera automatic exposure compensation method of claim 1, further comprising, before the step S1, a step S01: and setting an ideal brightness value in the target detection and identification system, wherein the ideal brightness value is used for comparing with the brightness value of the human face imaging position.
4. The automatic exposure compensation method for camera head according to claim 3, wherein the setting program of ideal brightness value is automatically changed according to time points according to a rule.
5. The camera automatic exposure compensation method of claim 3, further comprising, before the step S1, the step S02: and setting a preset value in the target detection and identification system, wherein the preset value is used for comparing the difference between the brightness value of the face imaging position and the ideal brightness value.
6. The camera automatic exposure compensation method of claim 5, wherein the preset value is a range value.
7. The camera automatic exposure compensation method of any one of claims 1 to 6, wherein in step S4, the adjusting camera gain and exposure parameters comprises: if the brightness value of the face imaging position is larger than the ideal brightness value, the camera gain and exposure parameter are reduced; and if the brightness value of the face imaging position is smaller than the ideal brightness value, the camera gain and exposure parameter are increased.
8. The camera automatic exposure compensation method of claim 7, wherein the exposure parameter of the camera includes at least one of aperture, shutter speed, and sensitivity.
9. The camera automatic exposure compensation device is applied to the method as claimed in claims 1 to 8, and is characterized by comprising a camera and a target detection and recognition system, wherein the target detection and recognition system comprises a human face imaging position acquisition module, a detection module and a judgment module;
the system comprises a camera, a face imaging position acquisition module, a detection module and a judgment module, wherein the camera is used for image acquisition, the face imaging position acquisition module is used for carrying out data acquisition training on face imaging characteristics in a strong backlight environment through a residual error network model in a deep learning target detection algorithm and generating a strong backlight face detection model, the detection module is used for detecting the brightness of a face imaging position, and the judgment module is used for judging whether a difference value between the brightness value of the face imaging position and an ideal brightness value meets a preset value or not; if so, the target detection and recognition system carries out face recognition on the image of the face imaging position; if not, firstly adjusting the camera gain and exposure parameters, and then repeating the steps S1-S4 until the face recognition is finished.
10. An electronic device comprising a processor, a storage medium and a bus, the storage medium storing machine-readable instructions executable by the processor, the processor and the storage medium communicating over the bus when the electronic device is operating, the processor executing the machine-readable instructions to perform the method of any one of claims 1 to 8.
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| CN114845052B (en) * | 2022-04-22 | 2024-03-12 | 杭州海康威视数字技术股份有限公司 | Shooting parameter adjustment method and device, camera and target equipment |
| CN116233619A (en) * | 2023-04-27 | 2023-06-06 | 北京城建智控科技股份有限公司 | Light supplementing control method applied to face recognition |
| CN116233619B (en) * | 2023-04-27 | 2023-07-14 | 北京城建智控科技股份有限公司 | Light supplementing control method applied to face recognition |
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